Object-Part Attention Model for Fine-Grained Image Classification

Object-centric Sampling for Fine-grained Image Classification

This paper proposes to go beyond the state-of-the-art deep convolutional neural network (CNN) by incorporating the information from object detection, focusing on dealing with fine-grained image classification. Unfortunately, CNN suffers from over-fiting when it is trained on existing finegrained image classification benchmarks, which typically only consist of less than a few tens of thousands t...

Weakly Supervised Learning of Part Selection Model with Spatial Constraints for Fine-Grained Image Classification

Attention for Fine-Grained Categorization

This paper presents experiments extending the work of Ba et al. (2014) on recurrent neural models for attention into less constrained visual environments, beginning with fine-grained categorization on the Stanford Dogs data set. In this work we use an RNN of the same structure but substitute a more powerful visual network and perform large-scale pre-training of the visual network outside of the...

Hierarchical Metric Learning for Fine Grained Image Classification

This paper deals with the problem of fine-grained image classification and introduces the notion of hierarchical metric learning for the same. It is indeed challenging to categorize fine-grained image classes merely in terms of a single level classifier given the subtle inter-class visual differences. In order to tackle this problem, we propose a two stage framework where i) the image categorie...

Incremental Fine-grained Information Status Classification Using Attention-based LSTMs

Information status plays an important role in discourse processing. According to the hearer’s common sense knowledge and his comprehension of the preceding text, a discourse entity could be old, mediated or new. In this paper, we propose an attention-based LSTM model to address the problem of fine-grained information status classification in an incremental manner. Our approach resembles how hum...